Collective Space-Sensing Coordinates Pattern Scaling in Engineered Bacteria.

Scale invariance refers to the maintenance of a constant ratio of developing organ size to body size. Although common, its underlying mechanisms remain poorly understood. Here, we examined scaling in engineered Escherichia coli that can form self-organized core-ring patterns in colonies. We found that the ring width exhibits perfect scale invariance to the colony size. Our analysis revealed a collective space-sensing mechanism, which entails sequential actions of an integral feedback loop and an incoherent feedforward loop. The integral feedback is implemented by the accumulation of a diffusive chemical produced by a colony. This accumulation, combined with nutrient consumption, sets the timing for ring initiation. The incoherent feedforward is implemented by the opposing effects of the domain size on the rate and duration of ring maturation. This mechanism emphasizes a role of timing control in achieving robust pattern scaling and provides a new perspective in examining the phenomenon in natural systems.

Synthetic biology looks good on paper.

Tremendous progress has been made in the design and implementation of synthetic gene circuits, but real-world applications of such circuits have been limited. Cell-free circuits embedded on paper developed by Pardee et al. promise to deliver specific and rapid diagnostics on a low-cost, highly scalable platform.

Engineered Ribonucleoprotein Granules Inhibit Translation in Protocells.

Liquid granules rich in intrinsically disordered proteins and RNA play key roles in critical cellular functions such as RNA processing and translation. Many details of the mechanism via which this occurs remain to be elucidated. Motivated by the lacuna in the field and by the prospects of developing de novo artificial granules that provide extrinsic control of translation, we report a bottom-up approach to engineer ribonucleoprotein granules composed of a recombinant RNA-binding IDP that exhibits phase behavior in water. We developed a kinetic model to illustrate that these granules inhibit translation through reversible or irreversible sequestration of mRNA. Within monodisperse droplets capable of transcription and translation, we experimentally demonstrate temporal inhibition of translation by using designer IDPs that exhibit tunable phase behavior. This work lays the foundation for developing artificial granules that promise to further our mechanistic understanding of their naturally occurring counterparts.

A generic approach to infer community-level fitness of microbial genes.

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes.

A 'rich-get-richer' mechanism drives patchy dynamics and resistance evolution in antibiotic-treated bacteria.

Bacteria in nature often form surface-attached communities that initially comprise distinct subpopulations, or patches. For pathogens, these patches can form at infection sites, persist during antibiotic treatment, and develop into mature biofilms. Evidence suggests that patches can emerge due to heterogeneity in the growth environment and bacterial seeding, as well as cell-cell signaling. However, it is unclear how these factors contribute to patch formation and how patch formation might affect bacterial survival and evolution. Here, we demonstrate that a 'rich-get-richer' mechanism drives patch formation in bacteria exhibiting collective survival (CS) during antibiotic treatment. Modeling predicts that the seeding heterogeneity of these bacteria is amplified by local CS and global resource competition, leading to patch formation. Increasing the dose of a non-eradicating antibiotic treatment increases the degree of patchiness. Experimentally, we first demonstrated the mechanism using engineered Escherichia coli and then demonstrated its applicability to a pathogen, Pseudomonas aeruginosa. We further showed that the formation of P. aeruginosa patches promoted the evolution of antibiotic resistance. Our work provides new insights into population dynamics and resistance evolution during surface-attached bacterial growth.

Programmable synthetic biomolecular condensates for cellular control.

The formation of biomolecular condensates mediated by a coupling of associative and segregative phase transitions plays a critical role in controlling diverse cellular functions in nature. This has inspired the use of phase transitions to design synthetic systems. While design rules of phase transitions have been established for many synthetic intrinsically disordered proteins, most efforts have focused on investigating their phase behaviors in a test tube. Here, we present a rational engineering approach to program the formation and physical properties of synthetic condensates to achieve intended cellular functions. We demonstrate this approach through targeted plasmid sequestration and transcription regulation in bacteria and modulation of a protein circuit in mammalian cells. Our approach lays the foundation for engineering designer condensates for synthetic biology applications.

Data-driven learning of structure augments quantitative prediction of biological responses.

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.

Vertical and horizontal gene transfer tradeoffs direct plasmid fitness.

Plasmid fitness is directed by two orthogonal processes-vertical transfer through cell division and horizontal transfer through conjugation. When considered individually, improvements in either mode of transfer can promote how well a plasmid spreads and persists. Together, however, the metabolic cost of conjugation could create a tradeoff that constrains plasmid evolution. Here, we present evidence for the presence, consequences, and molecular basis of a conjugation-growth tradeoff across 40 plasmids derived from clinical Escherichia coli pathogens. We discover that most plasmids operate below a conjugation efficiency threshold for major growth effects, indicating strong natural selection for vertical transfer. Below this threshold, E. coli demonstrates a remarkable growth tolerance to over four orders of magnitude change in conjugation efficiency. This tolerance fades as nutrients become scarce and horizontal transfer attracts a greater share of host resources. Our results provide insight into evolutionary constraints directing plasmid fitness and strategies to combat the spread of antibiotic resistance.

Horizontal gene transfer enables programmable gene stability in synthetic microbiota.

The functions of many microbial communities exhibit remarkable stability despite fluctuations in the compositions of these communities. To date, a mechanistic understanding of this function-composition decoupling is lacking. Statistical mechanisms have been commonly hypothesized to explain such decoupling. Here, we proposed that dynamic mechanisms, mediated by horizontal gene transfer (HGT), also enable the independence of functions from the compositions of microbial communities. We combined theoretical analysis with numerical simulations to illustrate that HGT rates can determine the stability of gene abundance in microbial communities. We further validated these predictions using engineered microbial consortia of different complexities transferring one or more than a dozen clinically isolated plasmids, as well as through the reanalysis of data from the literature. Our results demonstrate a generalizable strategy to program the gene stability of microbial communities.

Modulation of microbial community dynamics by spatial partitioning.

Microbial communities inhabit spatial architectures that divide a global environment into isolated or semi-isolated local environments, which leads to the partitioning of a microbial community into a collection of local communities. Despite its ubiquity and great interest in related processes, how and to what extent spatial partitioning affects the structures and dynamics of microbial communities are poorly understood. Using modeling and quantitative experiments with simple and complex microbial communities, we demonstrate that spatial partitioning modulates the community dynamics by altering the local interaction types and global interaction strength. Partitioning promotes the persistence of populations with negative interactions but suppresses those with positive interactions. For a community consisting of populations with both positive and negative interactions, an intermediate level of partitioning maximizes the overall diversity of the community. Our results reveal a general mechanism underlying the maintenance of microbial diversity and have implications for natural and engineered communities.

Programmable living assembly of materials by bacterial adhesion.

The field of engineered living materials aims to construct functional materials with desirable properties of natural living systems. A recent study demonstrated the programmed self-assembly of bacterial populations by engineered adhesion. Here we use this strategy to engineer self-healing living materials with versatile functions. Bacteria displaying outer membrane-anchored nanobody-antigen pairs are cultured separately and, when mixed, adhere to each other to enable processing into functional materials, which we term living assembled material by bacterial adhesion (LAMBA). LAMBA is programmable and can be functionalized with extracellular moieties up to 545 amino acids. Notably, the adhesion between nanobody-antigen pairs in LAMBA leads to fast recovery under stretching or bending. By exploiting this feature, we fabricated wearable LAMBA sensors that can detect bioelectrical or biomechanical signals. Our work establishes a scalable approach to produce genetically editable and self-healable living functional materials that can be applied in biomanufacturing, bioremediation and soft bioelectronics assembly.

Double-stranded RNA drives SARS-CoV-2 nucleocapsid protein to undergo phase separation at specific temperatures.

Nucleocapsid protein (N-protein) is required for multiple steps in betacoronaviruses replication. SARS-CoV-2-N-protein condenses with specific viral RNAs at particular temperatures making it a powerful model for deciphering RNA sequence specificity in condensates. We identify two separate and distinct double-stranded, RNA motifs (dsRNA stickers) that promote N-protein condensation. These dsRNA stickers are separately recognized by N-protein's two RNA binding domains (RBDs). RBD1 prefers structured RNA with sequences like the transcription-regulatory sequence (TRS). RBD2 prefers long stretches of dsRNA, independent of sequence. Thus, the two N-protein RBDs interact with distinct dsRNA stickers, and these interactions impart specific droplet physical properties that could support varied viral functions. Specifically, we find that addition of dsRNA lowers the condensation temperature dependent on RBD2 interactions and tunes translational repression. In contrast RBD1 sites are sequences critical for sub-genomic (sg) RNA generation and promote gRNA compression. The density of RBD1 binding motifs in proximity to TRS-L/B sequences is associated with levels of sub-genomic RNA generation. The switch to packaging is likely mediated by RBD1 interactions which generate particles that recapitulate the packaging unit of the virion. Thus, SARS-CoV-2 can achieve biochemical complexity, performing multiple functions in the same cytoplasm, with minimal protein components based on utilizing multiple distinct RNA motifs that control N-protein interactions.

Predicting plasmid persistence in microbial communities by coarse-grained modeling.

Plasmids are a major type of mobile genetic elements (MGEs) that mediate horizontal gene transfer. The stable maintenance of plasmids plays a critical role in the functions and survival for microbial populations. However, predicting and controlling plasmid persistence and abundance in complex microbial communities remain challenging. Computationally, this challenge arises from the combinatorial explosion associated with the conventional modeling framework. Recently, a plasmid-centric framework (PCF) has been developed to overcome this computational bottleneck. This framework enables the derivation of a simple metric, the persistence potential, to predict plasmid persistence and abundance. Here, we discuss how PCF can be extended to account for plasmid interactions. We also discuss how such model-guided predictions of plasmid fates can benefit from the development of new experimental tools and data-driven computational methods.

Temporal encoding of bacterial identity and traits in growth dynamics.

In biology, it is often critical to determine the identity of an organism and phenotypic traits of interest. Whole-genome sequencing can be useful for this but has limited power for trait prediction. However, we can take advantage of the inherent information content of phenotypes to bypass these limitations. We demonstrate, in clinical and environmental bacterial isolates, that growth dynamics in standardized conditions can differentiate between genotypes, even among strains from the same species. We find that for pairs of isolates, there is little correlation between genetic distance, according to phylogenetic analysis, and phenotypic distance, as determined by growth dynamics. This absence of correlation underscores the challenge in using genomics to infer phenotypes and vice versa. Bypassing this complexity, we show that growth dynamics alone can robustly predict antibiotic responses. These findings are a foundation for a method to identify traits not easily traced to a genetic mechanism.

Collective colony growth is optimized by branching pattern formation in Pseudomonas aeruginosa.

Branching pattern formation is common in many microbes. Extensive studies have focused on addressing how such patterns emerge from local cell-cell and cell-environment interactions. However, little is known about whether and to what extent these patterns play a physiological role. Here, we consider the colonization of bacteria as an optimization problem to find the colony patterns that maximize colony growth efficiency under different environmental conditions. We demonstrate that Pseudomonas aeruginosa colonies develop branching patterns with characteristics comparable to the prediction of modeling; for example, colonies form thin branches in a nutrient-poor environment. Hence, the formation of branching patterns represents an optimal strategy for the growth of Pseudomonas aeruginosa colonies. The quantitative relationship between colony patterns and growth conditions enables us to develop a coarse-grained model to predict diverse colony patterns under more complex conditions, which we validated experimentally. Our results offer new insights into branching pattern formation as a problem-solving social behavior in microbes and enable fast and accurate predictions of complex spatial patterns in branching colonies.

Universal antibiotic tolerance arising from antibiotic-triggered accumulation of pyocyanin in Pseudomonas aeruginosa.

Pseudomonas aeruginosa is an opportunistic pathogen that often infects open wounds or patients with cystic fibrosis. Once established, P. aeruginosa infections are notoriously difficult to eradicate. This difficulty is in part due to the ability of P. aeruginosa to tolerate antibiotic treatment at the individual-cell level or through collective behaviors. Here, we describe a new phenomenon by which P. aeruginosa tolerates antibiotic treatment. In particular, treatment of P. aeruginosa with sublethal concentrations of antibiotics covering all major classes promoted accumulation of the redox-sensitive phenazine pyocyanin (PYO). PYO in turn conferred general tolerance against diverse antibiotics for both P. aeruginosa and other gram-negative and gram-positive bacteria. This property is shared by other redox-active phenazines produced by P. aeruginosa. Our discovery sheds new insights into the physiological functions of phenazines and has implications for designing effective antibiotic treatment protocols.

Repulsive expansion dynamics in colony growth and gene expression.

Spatial expansion of a population of cells can arise from growth of microorganisms, plant cells, and mammalian cells. It underlies normal or dysfunctional tissue development, and it can be exploited as the foundation for programming spatial patterns. This expansion is often driven by continuous growth and division of cells within a colony, which in turn pushes the peripheral cells outward. This process generates a repulsion velocity field at each location within the colony. Here we show that this process can be approximated as coarse-grained repulsive-expansion kinetics. This framework enables accurate and efficient simulation of growth and gene expression dynamics in radially symmetric colonies with homogenous z-directional distribution. It is robust even if cells are not spherical and vary in size. The simplicity of the resulting mathematical framework also greatly facilitates generation of mechanistic insights.

Versatile biomanufacturing through stimulus-responsive cell-material feedback.

Small-scale production of biologics has great potential for enhancing the accessibility of biomanufacturing. By exploiting cell-material feedback, we have designed a concise platform to achieve versatile production, analysis and purification of diverse proteins and protein complexes. The core of our technology is a microbial swarmbot, which consists of a stimulus-sensitive polymeric microcapsule encapsulating engineered bacteria. By sensing the confinement, the bacteria undergo programmed partial lysis at a high local density. Conversely, the encapsulating material shrinks responding to the changing chemical environment caused by cell growth, squeezing out the protein products released by bacterial lysis. This platform is then integrated with downstream modules to enable quantification of enzymatic kinetics, purification of diverse proteins, quantitative control of protein interactions and assembly of functional protein complexes and multienzyme metabolic pathways. Our work demonstrates the use of the cell-material feedback to engineer a modular and flexible platform with sophisticated yet well-defined programmed functions.

Engineered microbial consortia: strategies and applications.

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

A noisy linear map underlies oscillations in cell size and gene expression in bacteria.

During bacterial growth, a cell approximately doubles in size before division, after which it splits into two daughter cells. This process is subjected to the inherent perturbations of cellular noise and thus requires regulation for cell-size homeostasis. The mechanisms underlying the control and dynamics of cell size remain poorly understood owing to the difficulty in sizing individual bacteria over long periods of time in a high-throughput manner. Here we measure and analyse long-term, single-cell growth and division across different Escherichia coli strains and growth conditions. We show that a subset of cells in a population exhibit transient oscillations in cell size with periods that stretch across several (more than ten) generations. Our analysis reveals that a simple law governing cell-size control-a noisy linear map-explains the origins of these cell-size oscillations across all strains. This noisy linear map implements a negative feedback on cell-size control: a cell with a larger initial size tends to divide earlier, whereas one with a smaller initial size tends to divide later. Combining simulations of cell growth and division with experimental data, we demonstrate that this noisy linear map generates transient oscillations, not just in cell size, but also in constitutive gene expression. Our work provides new insights into the dynamics of bacterial cell-size regulation with implications for the physiological processes involved.